Image Super-Resolution.
Many existing SR algorithms [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]} directly learn a mapping from the low-resolution images to high-resolution images with a pixel-wise constraint (\(\ell _2\)  loss).
While these methods achieve remarkable results in terms of PSNR, training solely with pixel-wise constraints often results in perceptually unconvincing outputs with severe over-smoothing artifacts [9]}, [10]}.
To alleviate the problem, GANs [9]}, [12]}, [13]}, [14]} are employed to approximate the natural image manifold, yielding more photo-realistic results. However, as the generator needs to learn both fidelity and natural image characteristics, unnatural artifacts could still be observed in the outputs, especially if one trains the generator from scratch.
